the influence of head impact threshold for reporting data
TRANSCRIPT
ORIGINAL RESEARCH ARTICLE
The Influence of Head Impact Threshold for Reporting Datain Contact and Collision Sports: Systematic Review and OriginalData Analysis
D. King1,2 • P. Hume1 • C. Gissane3 • M. Brughelli1 • T. Clark4
Published online: 6 November 2015
� Springer International Publishing Switzerland 2015
Abstract
Background Head impacts and resulting head accelera-
tions cause concussive injuries. There is no standard for
reporting head impact data in sports to enable comparison
between studies.
Objective The aim was to outline methods for reporting
head impact acceleration data in sport and the effect of the
acceleration thresholds on the number of impacts reported.
Methods A systematic review of accelerometer systems
utilised to report head impact data in sport was conducted.
The effect of using different thresholds on a set of impact
data from 38 amateur senior rugby players in New Zealand
over a competition season was calculated.
Results Of the 52 studies identified, 42 % reported
impacts using a[10-g threshold, where g is the accelera-
tion of gravity. Studies reported descriptive statistics as
mean ± standard deviation, median, 25th to 75th
interquartile range, and 95th percentile. Application of the
varied impact thresholds to the New Zealand data set
resulted in 20,687 impacts of [10 g, 11,459 (45 % less)
impacts of[15 g, and 4024 (81 % less) impacts of[30 g.
Discussion Linear and angular raw data were most fre-
quently reported. Metrics combining raw data may be more
useful; however, validity of the metrics has not been ade-
quately addressed for sport. Differing data collectionmethods
and descriptive statistics for reporting head impacts in sports
limit inter-study comparisons. Consensus on data analysis
methods for sports impact assessment is needed, including
thresholds. Based on the available data, the 10-g threshold is
the most commonly reported impact threshold and should be
reported as themedianwith 25th and 75th interquartile ranges
as the data are non-normally distributed. Validation studies
are required to determine the best threshold and metrics for
impact acceleration data collection in sport.
Conclusion Until in-field validation studies are com-
pleted, it is recommended that head impact data should be
reported as median and interquartile ranges using the
10-g impact threshold.
Key Points
The validity of head impact metrics has not been
adequately addressed for sports.
Consensus on data analysis methods is required for
reporting head impact biomechanics.
As head impact data are not normally distributed, to
allow for comparison between studies, it is
recommended that these data be reported using
median values and interquartile ranges.
It is recommended that a 10-g linear threshold be
utilised for the reporting of head impact
biomechanics.
Electronic supplementary material The online version of thisarticle (doi:10.1007/s40279-015-0423-7) contains supplementarymaterial, which is available to authorized users.
& D. King
1 Sports Performance Research Institute New Zealand, School
of Sport and Recreation, Faculty of Health and
Environmental Sciences, Auckland University of
Technology, Auckland, New Zealand
2 Emergency Department, Hutt Valley District Health Board,
Private Bag 31-907, Lower Hutt, New Zealand
3 School of Sport, Health and Applied Science, St Mary’s
University, Twickenham, Middlesex, UK
4 Faculty of Human Performance, Australian College of
Physical Education, Sydney Olympic Park, NSW, Australia
123
Sports Med (2016) 46:151–169
DOI 10.1007/s40279-015-0423-7
1 Introduction
1.1 Head Impacts Cause Injury: Evidence
Known as the ‘silent injury’ [1], and often reported by the
media and sporting circles as a ‘knock to the head’ [2],
sport-related concussions (hereafter called ‘concussion’)
are a subset of mild traumatic brain injuries (mTBIs) [3]
and have become an increasingly serious concern for all
sporting activities worldwide [4–6]. Research into con-
cussions [7] has increased over the years, leading to greater
insight into the causes and the effects of these injuries.
Research [8–27] has sought to better determine the head
linear and rotational accelerations involved in concussion
injuries through the use of telemetry. By adapting radio-
telemetry that was utilised for astronauts [28], a telemetry
system was developed and has been in use since 1961 for
the recording of impacts for football players and concus-
sions [29] that have occurred.
1.2 A Cumulative Head Impact Threshold may be
Related to Concussion
The immediate and long-term effects of multiple and
repeated blows to the head that athletes receive in contact
sporting environments are a growing concern in clinical
practice [30, 31]. Concern has grown about the effects of
subconcussive impacts to the head and how these impacts
may adversely affect cerebral functions [30–32]. Subcon-
cussive events are impacts that occur where there is an
apparent brain insult with insufficient force to result in the
hallmark signs and symptoms of a concussion [31, 33, 34].
Although subconcussive events do not result in observable
signs and apparent behavioural alterations [35, 36], they
can cause damage to the central nervous system and have
the potential to transfer a high degree of linear and rota-
tional acceleration forces to the brain [37]. Proposed dec-
ades previously [38, 39], exposure to repetitive
subconcussive blows to the head may result in similar, if
not greater damage than a single concussive event [33] and
may have cumulative effects [40].
Participants can be exposed to a high number of impacts
per season [32]. It has been suggested [41, 42] that brain
injuries come from concussive events and also from the
accumulation of subconcussive impacts that result in
pathophysiological changes in the brain. As subconcussive
impacts do not result in observable concussion-related
signs and symptoms, these are often not medically diag-
nosed. The accumulation of subconcussive blows can result
in neuropsychological changes [30, 31, 42–46]. However,
similar to the literature focused on concussion and mTBI,
the literature on subconcussive head trauma is limited [47].
What is not known is the number of head impacts and their
intensity that might lead to concussion (i.e. a concussion
cumulative threshold). The injury threshold is likely to be
different for each person given the multifactorial nature of
injuries, as per other thresholds for injuries to tendons,
ligaments, muscle and bone. If a threshold could be
determined, players could be monitored to reduce their
potential risk for concussion injury—akin to cricket mon-
itoring players’ loading to the body during bowling events
via the number of overs in an attempt to reduce the risk of
back stress fractures [48].
1.3 Impacts can be Measured with a Number
of Technologies
Head impact dynamics have been analysed through the use
of video analysis [8], in game measurements [20–25, 27,
49–52], numerical methods [9–12] and reconstructions
using anthropometric test devices [13–19] in helmeted
sports such as American football [20–23] and ice hockey
[24, 25] and in un-helmeted sports such as soccer [26] and
rugby union [27].
The on-field assessment of head impacts has been
enabled with a head impact telemetry system (HITS)
(Simbex, LLC, Lebanon, NH), using helmet-mounted
accelerometers enabling determination of the head linear
and rotational accelerations in American football [21, 23,
49, 53–55] and ice hockey [24, 25], and using a headband
in youth soccer [26]. The data collected through the HITS
has enabled analytical risk functions [16, 51, 56, 57],
concussion risk curves [51], and risk weighted exposure
metrics [58] to be developed, further assisting in the
identification of sports participants at risk of concussive
injuries. More recently, instrumented mouthguards known
as XGuard (X2biosystems, Inc., Seattle, WA, USA) have
documented head impacts in rugby union [27].
1.4 Thresholds have Differed for Reporting Impact
Data in Contact and Collision Sports
Although there is an increasing amount of published lit-
erature reporting impact accelerations to the head in the
sporting environment, there is less attention focussed on
identifying what is a subconcussive impact and where this
occurs. Studies [55, 59, 60] have been conducted reporting
the impacts absorbed by the head during activities under-
taken daily. Although impacts to the head and body under
10 g have been reported [55], these activities such as
walking, jumping, running and sitting are considered to be
non-contact events [21, 61]. However, impacts greater than
10 g occurring from contact events that do not result in
acute signs or symptoms of concussion are identified as
subconcussive impacts [43].
152 D. King et al.
123
1.5 To Enable Comparison of Studies, a Consistent
Threshold for Reporting is Needed
Head impact data are essential to understand the biome-
chanics of head injury to develop potential injury preven-
tion strategies. Researchers have utilised different
thresholds, with the most common being 9.6 and 14.4 g,
depending on the accelerometer. The equipment utilised to
record and report head impacts varies in sensitivity and the
types of algorithms employed for the identification of
impacts [62]. These differences may invariably influence
the results of the published studies as, although some
studies report the linear threshold as 14.4 g, they may
actually be recording from 10 g, and if the researcher is
unaware that this threshold is the default, the data may be
included (personal correspondence, S. Broglio; September
2015). The collection of the impact data is based on one
accelerometer and the unfiltered/unprocessed data, and the
value obtained only loosely relates to the final measure
being sought. The impact data are processed with a hard
exclusion cut-off of 10 g, enabling data collection to
become manageable as acceleration lower than 10 g with-
out impacts occurring becomes more common (personal
correspondence, S. Broglio; 22 September 2015). There is
currently no standard for reporting head impact data to
enable comparison between studies. Currently, the use of
accelerometers may not necessarily provide the meaningful
inter-study comparisons that are sought, because of data
collection, processing and methodologies not being stan-
dardised [63]. Studies utilising different impact thresholds
have proposed varying conclusions based on the method-
ological and reporting approaches undertaken.
1.6 Measurements Reporting Head Impact
Biomechanics and Injury Causation
In 1966, Gadd [64] proposed the Gadd Severity Index
(GSI) head injury severity index based on the Wayne State
Tolerance Curve (WSTC). Developed from animal and
cadaver impact data, the GSI simplified the WSTC by
taking into consideration the shape of the linear accelera-
tion time history, providing a weighting factor of 2.5,
enabling the whole body acceleration data to be plotted on
log-log coordinates along a straight line. The critical value
of the GSI is 1000. If the GSI is less than 1000, the head
impact is considered probabilistically safe. The GSI is used
to quantify severe skull fractures and brain injury risk, but
is not recommended for use to quantify a risk of concussion
[65]. A concern with regard to the GSI is that it can give
unrealistically high values for impacts that have a much
longer pulse duration [66]. The mathematical expression
for the GSI is:
GSI ¼ZT
0
aðtÞ2:5dt
where a is the ‘effective’ acceleration (thought to have
been the average linear acceleration) of the head measured
in terms of g, the acceleration of gravity, and t is the time in
milliseconds from the start of the impact [67].
In 1971, a modification of the GSI, the Head Injury
Criterion (HIC), was proposed [68] to focus the severity
index on that part of the impact that was likely to be rel-
evant to the risk of injury to the brain. This was done by
averaging the integration of the resultant acceleration/time
curve over whatever time interval yielded the maximum
value of HIC. Because this varies from one impact to
another, the expression for the modified index simply refers
to times t1 and t2. The HIC is computed based on the
following expression:
HIC ¼ 1
t2 � t1
Zt2
t1
a tð Þdt
24
355=2
t2 � t1ð Þ
where t2 and t1 are any two arbitrary time points during the
acceleration pulse. Acceleration is measured in multiples
of g, and time is measured in seconds. The resultant
acceleration is used for the calculation. The US National
Highway Traffic Safety Administration (NHTSA) requires
t2 and t1 to not be more than 36 ms apart (thus called
HIC36) and the maximum HIC36 to not exceed 1000. In
1998 [69], the NHTSA introduced the HIC15, where t2 and
t1 are not to be more than 15 ms apart and the maximum
HIC15 is not to exceed 700. In a numerical study [70], it
was estimated that an mTBI tolerance for the HIC15, where
there is a 25, 50 and 75 % likelihood of an mTBI occur-
ring, had HIC15 values of 136, 235 and 333, respectively.
In 2008 [71], the principal component score (PCS), a
weighted sum of linear acceleration, rotational accelera-
tion, HIC and GSI, with objectively defined weights, was
published. It is now more commonly termed the Head
Impact Telemetry Severity Profile (HITSP), and is a
weighted composite score including linear and rotational
accelerations, impact duration, as well as impact location.
The resulting formula is:
HITSP ¼ 10� ð0:4718� sGSIþ 0:4742� sHICþ 0:4336� sLIN þ 0:2164� sROTÞ þ 2
where sX = (X-mean [X])/(SD [X]), LIN = linear accel-
eration, ROT = rotational acceleration, HIC = Head
Injury Criterion, and GSI = Gadd Severity Index. The
offset by 2 and scaling by 10 generates HITSP values
greater than 0 and in the numerical range of the other
classic measures studied. A HITSP score of 63 or greater is
Reporting Impact Data in Sport 153
123
reported to be an indication there is a 75 % risk of a
concussive injury occurring [71].
In 2013, a novel cumulative exposure metric, the Risk
Weighted Cumulative Exposure (RWE) equation, was
developed [58] with four previously published analytical
risk functions. The four different analytical risk functions
were the linear resultant acceleration [16, 56], rotational
resultant acceleration [51] and combined probability (linear
and rotational) resultant accelerations [57]. These risk
functions were utilised to elucidate individual player and
team-based exposure to head impacts. The RWE equations
comprise of aL as the measured peak linear acceleration
(PLA), aR as the measured peak rotational acceleration
(PRA), and nhits as the number of head impacts in a season
for a given player.
Risk function(s) Equation
Linear [16, 56] RWELinear =
Pnhitsi¼1 R aLð Þi
Rotational [51] RWERotational =
Pnhitsi¼1 R aRð Þi
Combined probability [57] RWECP =
Pnhitsi¼1 CP aL; aRð Þi
RWE Risk weighted exposure, CP combined probability
In the logistic regression equations and regression
coefficients of the injury risk functions utilised in the
prediction of injury, a and b are the regression coefficients
and x is the measured acceleration for the linear and ro-
tational risk functions [58].
Logistic regression
equation
Risk function Regression coefficients
R a½ � ¼ 11þe�aþbx Linear [16,
56]
a = -9.805, b = 0.0510
Rotational
[51]
a = -12.531, b = 0.0020
CP ¼ 11þe�ðb0þb1aþb2aþb3aa
Combined
probability
(CP) [57]
b0 = -10.2, b1 = 0.0433,
b2 = 0.000873, b3 = -
9.2E-07
b0, b1, b2 and b3 are regression coefficients, a is the
measured linear acceleration, and a is the measured rota-
tional acceleration for the combined probability risk
function. The three metrics provided as a result of these
equations are for linear (RWELinear), rotational (RWERota-
tional) and combined (linear and rotational) probability
(RWECP).
In an attempt to delineate injury causation and to
establish a meaningful injury criterion through the use of
actual field data, Zhang et al. [12]. proposed tolerance
levels for human head injury based on input kinematics
scaled from animal data and non-injurious volunteer test
results. Injury predictors and injury levels were analysed on
the basis of resulting brain tissue responses, and these were
correlated with the site and occurrence of a concussion.
The calculated sheer stress around the brainstem region
could be an injury predictor, and statistical analyses were
performed to establish a brain injury tolerance level. As a
result of the analyses undertaken, and based on linear
logistic regression analyses, it was reported [12] that the
maximum resultant translational acceleration at the centre
of gravity of the head was estimated to be 66, 82 and
106 g for a 25, 50 and 80 % probability of sustaining an
mTBI, respectively.
For resultant rotational acceleration at the centre of
gravity of the head, this was estimated to be 4600 radians
per second per second (rad/s2), 5900 rad/s2 and 7900 rad/s2
for a 25, 50 and 80 % probability of sustaining an mTBI,
respectively. The estimated HIC15 thresholds were 151,
240 and 369 for a 25, 50 and 80 % probability of sustaining
an mTBI, respectively. These thresholds are considerably
less than the HIC15 limit of 1000 for sustaining a serious
brain injury. If the head was exposed to a combined
translational and rotational acceleration with an impact
duration between 10 and 30 ms, the suggested tolerable
reversible brain injury was 85 g translational acceleration,
6000 rad/s2 rotational acceleration and an HIC15 value of
240. These values may change as more human data become
available, but to date no published updates of these values
have been available. Although other variables have been
proposed (Generalised Acceleration Model for Brain Injury
Threshold [GAMBIT] [14, 72, 73], and Head Impact Power
[HIP] [74]), these have not been utilised in any studies
reporting head impacts in contact sport.
1.7 Aim of the Study
The rationale for this study is based on questions around
the magnitude of a single impact that may result in con-
cussion, the number of impacts needed to result in signs
and symptoms of concussion, and individual player dif-
ferences that might affect injury tolerance levels for con-
cussion. Given head impacts are likely to cause concussive
injury, and the number of head impacts may be related to a
potential concussion threshold (i.e. a cumulative thresh-
old), the number of head impacts should be monitored in
players. However, given impacts can be measured with a
number of technologies (e.g. instrumented behind-the-ear
patches, mouthguards, and head gear) and thresholds have
differed for reporting impact data in contact and collision
sports, a threshold for reporting impact data in sport is
needed to enable comparison of studies. Therefore, the
aims of this study were to (a) summarise the methods for
reporting head impact data in sport to date and (b) assess
154 D. King et al.
123
the impact of different acceleration thresholds on the likely
identification of concussive injuries.
2 Methods
To outline methods for reporting head impact data, a sys-
tematic review of the literature was conducted. The
guideline for reporting observational studies [Meta-analy-
sis Of Observational Studies in Epidemiology (MOOSE)]
[75] was followed for the empirical literature evidence
included in this study. The MOOSE checklist contains
specifications and guidelines for the conduct and review of
the studies. To evaluate the effects of acceleration thresh-
olds on the number of impacts reported, variable thresholds
were applied to head impact data obtained from 38 senior
amateur rugby union players during 19 matches in New
Zealand [27].
2.1 Literature Review to Identify Thresholds
for Reporting Head Impact Data in Contact
and Collision Sport
2.1.1 Search Strategy for Identification of Publications
A total of 53,185 studies available online from January
1990 to June 2015, identified through the SCOPUS
(n = 10,090), SportDiscus (n = 1187), OVID (n = 9729),
Science Direct (n = 27,803) and Health Sciences
(n = 4376) databases, were screened for eligibility (see
Fig. 1). The keywords utilised for the search of relevant
research studies included combinations of ‘head impact
telemetry system*’, ‘HITS’, ‘concussion’, ‘impact*’,
‘traumatic brain injury’, ‘chronic traumatic encephalopa-
thy’, ‘angular’, ‘linear’, ‘rotational’, ‘acceleration’,
‘biomechanics’, ‘head acceleration’ and ‘risk’. An example
of the Health Sciences search strategy is provided in the
Electronic Supplementary Material (ESM), Table S1.
Searches were limited to ‘English language’ and ‘humans’
only. The references of all relevant articles were searched
for further articles. All publications identified were initially
screened by publication title and abstract to identify eli-
gibility. In cases of discrepancies of eligibility, another
author assessed the publication to screen for eligibility.
To establish some control over heterogeneity of the
studies [75], inclusion criteria were established. Any pub-
lished study or book that did not meet the inclusion criteria
was excluded from the study. Publications were included if
they reported head impact biomechanics and met the fol-
lowing inclusion criteria:
1. The study was published in a peer reviewed journal or
book.
2. The study reported the biomechanics of impacts to the
head in a sporting environment.
3. The study addressed one or more of the keywords used
in the search strategy relating to this study.
Reviewed studies were excluded from this review if it
was identified that the publication:
1. Was unavailable in English.
2. Did not provide additional information specifically
addressing areas relating to this study.
3. Was a case study.
4. Reviewed head impact studies.
2.1.2 Assessment of Publication Quality
The 52 studies [10, 12, 16, 20–27, 32, 37, 42, 49, 51–54,
57, 58, 61, 71, 73, 76–103] meeting the inclusion criteria
(see Table 1) were assessed for quality by two of the
authors on the basis of the MOOSE [75] published
checklist. Heterogeneity of the studies included in the
literature review was expected as there might be differ-
ences in the study design, population and outcomes [75].
As a result of the MOOSE [75] checklist, the studies
included had a median score of 4.8/6.0, with a range of
4.0–5.0.
2.2 Ethical Consent and Mouthguard
Instrumentation
2.2.1 Participants and Ethical Consent
A prospective observational cohort study was conducted on
a premier club level amateur rugby union team during the
2013 domestic competition season of matches in New
Zealand. All thirty-eight male players [mean ± standard
deviation (SD) age 22 ± 4 years] were amateurs receiving
no remuneration for participating in rugby union activities.
The matches were played under the laws of the New
Zealand Rugby Football Union. All players involved in the
team under study were invited to participate and were free
to withdraw at any stage of the study. All participating
players signed a consent form before being provided with
the mouthguard. If players withdrew from the study, they
were still eligible for participation in the match activities of
the team under study. No players withdrew from the study.
The researchers’ university ethics committee approved all
procedures in the study (AUTEC 12/156), and all players
gave informed consent prior to participating. All proce-
dures followed were in accordance with the ethical stan-
dards of the responsible committee on human
experimentation (institutional and national) and with the
Helsinki Declaration of 1975, as revised in 2013.
Reporting Impact Data in Sport 155
123
2.2.2 Mouthguard Instrumentation
Players were fitted with a moulded X2Biosystems All-In-
Mouth (AIM) instrumented mouthguard (X2Biosystems,
Inc., Seattle, WA, USA), sampling at 1000 Hz, prior to the
start of the season. The mouthguards contained a low-
power, high-g tri-axial accelerometer (H3LIS331DL) with
200 g maximum per axis, and a tri-axial angular rate
gyroscope (L3G4200D; ST Microelectronics, Geneva,
Switzerland; http://www.st.com) [104]. The mouthguards
utilised were similar to those utilised in a previous study
[104]. The accelerometer and gyroscope calculated an
acceleration and rotational time history of the head’s esti-
mated centre of gravity for all impacts that occurred during
match participation. The time history incorporated three
axes (x, y, z) of acceleration and three axes of velocity.
With the player standing upright, these planes described
sideways (medio-lateral), forward–back (anterior–poste-
rior) and vertical acceleration and deceleration. The
mouthguards [104] have strong correlations for PLA
(r2 = 0.93), PRA (r2 = 0.88) and peak rotational velocity
(r2 = 0.97) when compared with the head’s centre of
gravity.
The moulded instrumented mouthguards have been
reported [104, 105] to have normalised root-mean-square
errors for impact time traces of 9.9 ± 4.4 % for linear
acceleration, 9.7 ± 7.0 % for angular acceleration, and
10.4 ± 9.9 % for angular velocity, but miss, or misclas-
sify, *4 % of impacts [106]. The average error offset
for impact location was 1.63� ± 3.74� azimuth and
-1.57� ± 0.48� elevation [105]. The mouthguards recor-
ded head linear and rotational acceleration, impact location
and duration. For the impact to be recorded, a total of
100 ms of data were stored, including 25 ms prior to, and
75 ms following, the impact. Software provided by
X2Biosystems calculated the PLA, PRA (x axis and y axis
angular accelerations), impact location, HIC [68] and GSI
[64], and these were date and time stamped for later
download and analyses. All data were recorded on the
X2Biosystem Injury Management Software (IMS) and
Records identified (n = 53,185)
Scre
enin
g In
clud
ed
Elig
ibili
ty
Iden
tific
atio
n
Records after duplicates removed, with titles screened for eligibility
(n = 22,590)
Records excluded (n = 19,943)
Abstracts screened for eligibility (n = 2,647)
Abstracts excluded (n = 2,521) Not published in English (n = 30)
No additional information provided (n = 2,491)
Studies included (n = 52)
Full-text articles assessed for eligibility (n = 126)
Full-text articles excluded (n = 74) Not directly related to study (n = 53)
Not head impact biomechanics (n = 13) Review of previous studies (n = 8)
Fig. 1 Flow of identification,
screening, eligibility and study
inclusion of previously
published studies
156 D. King et al.
123
Table
1MOOSEscores,dataacquisitionim
pactthresholds,studygroups,sportingcodes
andduration,instrumentedequipment,participantnumbers,im
pactsrecorded
fortotalandper
player
andmetrics
forreportingdata
Study
MOOSE
[75]score
Data
acquisition
limit(g)
Study
group
Sport,no.
seasons
No.
participants
Impacts
total
Impactsper
player
Raw
data
Derived
variables
Reportingstatistics
PLA
(g)
PRA
(rad/
s2)
HIC
15
HIC
36
GSI
HIT
SP
Mean
(SD)
Median
IQR
95%
Other
Hernandez
etal.[73]
4/6 (6
7%)
7;10
Coll;P
Am$,
MM,B
2concussions;
T513
YY
YY
Brolinsonetal.
[76]
5/6 (8
3%)
10
Coll
Am$,2
52
11,604
T223a
YY
Bazarianet
al.
[77]
5/6 (8
3%)
10
Coll
Am$,1
10
9769
T;977a
YY
Y
Criscoet
al.
[21]
5/6 (8
3%)
10
Coll
Am$,3
314
286,636
T420;bP250;b
M128b
YY
YY
YY
Criscoet
al.
[78]
5/6 (8
3%)
10
Coll
Am$,2
254
184,358
T:726a
YY
YY
Danielet
al.
[54]
5/6 (8
3%)
10
Youth
Am$,1
7748
T107;P63;M
44
YY
YY
Y
Dumaet
al.
[49]
5/6 (8
3%)
10
Coll
Am$,1
38
3312
T87a
YY
YY
Funketal.[79]
5/6 (8
3%)
10
Coll
Am$,4
98
37,128
T379a
YY
Gwin
etal.
[80]
5/6 (8
3%)
10
Coll
IH,#,2
12
4393
T5
YY
HanlonandBir
[26]
5/6 (8
3%)
10H
Youth
Soccer,P
24
47H
20
NH
N/S
YY
YY
Harpham
etal.
[81]
5/6 (8
3%)
10
Coll
Am$,1
38
N/S
N/S
YY
YY
Kingetal.[27]
5/6 (8
3%)
10M
Snr Amat
RU,1
38
20,687
T564;M
77
YY
Y
Mihalik
etal.
[61]
5/6 (8
3%)
10
Coll
Am$,2
72
57,024
T9504a
YY
Y
Mihalik
etal.
[82]
5/6 (8
3%)
10
Youth
IH,1
37
7770
T1945a
YY
YY
Mihalik
etal.
[83]
5/6 (8
3%)
10
Youth
IH,2
52
12,253
T223;bP83;b
M24b
YY
YY
YY
Mihalik
etal.
[84]
5/6 (8
3%)
10
Youth
IH,1
16
4608
T288a
YY
YY
Y
Munce
etal.
[85]
5/6 (8
3%)
10
Youth
Am$,1
22
6183
T281a
YY
YY
YY
Ocw
ieja
etal.
[86]
5/6 (8
3%)
10
Coll
Am$,1
46
7992
T174a
YY
YY
Y
Reedetal.[24]
5/6 (8
3%)
10
Youth
IH,1
13
1821
T140;M
5Y
YY
YY
Rowsonet
al.
[22]
5/6 (8
3%)
10
Coll
Am$,1
10
1712
T171a
YY
YY
Reporting Impact Data in Sport 157
123
Table
1continued
Study
MOOSE
[75]score
Data
acquisition
limit(g)
Study
group
Sport,no.
seasons
No.
participants
Impacts
total
Impactsper
player
Raw
data
Derived
variables
Reportingstatistics
PLA
(g)
PRA
(rad/
s2)
HIC
15
HIC
36
GSI
HIT
SP
Mean
(SD)
Median
IQR
95%
Other
Schnebel
etal.
[52]
5/6 (8
3%)
10
Coll
Am$,1
40
54,154
T1354a
YY
10
HS
Am$,1
16
8326
T520a
YY
Beckwithet
al.
[87,88]
5/6 (8
3%)
14.4
Coll/
HS
Am$,6
95
161,732
T1702a
YY
YY
Y
Broglioet
al.
[89]
5/6 (8
3%)
14.4
HS
Am$,1
42
32,510
T744;P11cM
24
YY
Y
Cobbet
al.
[20]
5/6 (8
3%)
14.4
Youth
Am$,1
50
11,978
T240;P10;M
11
YY
YY
Y
Criscoet
al.
[53]
5/6 (8
3%)
14.4
Coll
Am$,1
188
3878
T21;aP6;M
14
Y
Danielet
al.
[90]
5/6 (8
3%)
14.4
Youth
Am$,1
17
4678
T275;aP163;
M112
YY
YY
Y
Rowsonet
al.
[51]
5/6 (8
3%)
14.4
Coll
Am$,2
314
300,977
T959a
Y
Talavageet
al.
[42]
5/6 (8
3%)
14.4
HS
Am$,1
21
15,264
T727a
Urban
etal.
[58]
5/6 (8
3%)
14.4
HS
Am$,1
40
16,502
T413a
YY
YY
YY
Younget
al.
[23]
5/6 (8
3%)
14.4
Youth
Am$,1
19
3059
T161;P95;M
65
YY
YY
YY
Broglioet
al.
[91]
5/6 (8
3%)
15
HS
Am$,3
78
54,247
T695a
YY
YY
Broglioet
al.
[92]
5/6 (8
3%)
15
HS
Am$,1
35
19,224
T549;aP9;M
25
YY
Y
Broglioet
al.
[37,93]
5/6 (8
3%)
15
HS
Am$,4
95
101,994
T652
YY
YY
Y
Eckner
etal.
[94]
5/6 (8
3%)
15
HS
Am$,2
20
30,298
T1515a
YY
YY
YY
YY
Martiniet
al.
[95]
5/6 (8
3%)
15
HS
Am$,2
83
35,620
T429a
YY
YY
Wilcoxet
al.
[25]
5/6 (8
3%)
20
Coll
IH#/$,3
91
37,411
T19,980d/
17,531e
YY
YY
YY
Y
Wilcoxet
al.
[96]
5/6 (8
3%)
20
Coll
IH#/$,1/
354
616
T270d/242
YY
YY
Wonget
al.
[97]
5/6 (8
3%)
30
Youth
Am$,1
22
480
T22;aP4;M
2Y
Y
Gyslandet
al.
[32]
5/6 (8
3%)
\60[90
Coll
Am$,1
46
N/S
T1177;12
[90g
YY
McC
affrey
etal.[98]
5/6 (8
3%)
\60[90
Coll
Am$,1
43
N/S
N/S
YY
Y
158 D. King et al.
123
Table
1continued
Study
MOOSE
[75]score
Data
acquisition
limit(g)
Study
group
Sport,no.
seasons
No.
participants
Impacts
total
Impactsper
player
Raw
data
Derived
variables
Reportingstatistics
PLA
(g)
PRA
(rad/
s2)
HIC
15
HIC
36
GSI
HIT
SP
Mean
(SD)
Median
IQR
95%
Other
Frechedeand
McIntosh
[10]
4/6 (6
7%)
Recon
Prof
AFL/RU,
3–
–N/S
YY
YY
McIntosh
etal.
[99]
4/6 (6
7%)
Recon
Prof
AFL,3
––
N/S
YY
Y
Pellm
anet
al.
[16]
4/6 (6
7%)
Recon
Prof
Am$,5
––
N/S
YY
YY
Zhanget
al.
[12]
4/6 (6
7%)
Recon
Lab
––
––
YY
YY
Y
Breedlove
etal.[100]
4/6 (6
7%)
N/S
HS
Am$,2
24
N/S
N/S
YY
YY
Duhaimeet
al.
[101]
4/6 (6
7%)
N/S
Coll
Am$,IH
,4
450
486,594
T1081a
YY
Greenwald
etal.[71]
4/6 (6
7%)
N/S
Coll/
HS
Am$,3
449
17concussions
only
YY
YY
Y
Guskiewicz
etal.[102]
4/6 (6
7%)
N/S
Coll
Am$,2
88
104,714
T1190a
YY
Rowsonand
Duma[57]
4/6 (6
7%)
N/S
Coll
Am$
N/S
63,011
Combined
data
YY
Y
Wilcoxet
al.
[103]
4/6 (6
7%)
N/S
Coll
IH$,3
58
9concussions
YY
YY
Meanstudy
quality
4.8
±0.4
(79.6
%±
7.0)
Percentageof
studies
92.2
74.5
17.6
3.9
3.9
29.4
52.9
21.6
7.8
23.5
54.9
Instrumentedequipmentusedis
helmet
unless
thedataacquisitionlimitisRecon,ormouthguard(M)orheadband(H)
95%
95th
percentile,AFLAustralian
FootballLeague,
AmFAmerican
football,Bboxing,Collcollegiate,gaccelerationofgravity,GSIGaddSeverityIndex,H
header,HIC
15HeadIm
pactCriterion
15ms,HIC
36HeadIm
pactCriterion36ms,HIT
SPHeadIm
pactTelem
etry
SeverityProfile,HShighschool,IH
icehockey,IQ
Rinterquartile
range,
Mmatch
impacts,MM
mixed
martial
arts,MOO-
SEMeta-analysisOfObservational
Studiesin
Epidem
iology,N/S
notstated,NH
non-header,Ppracticeim
pacts,PLA(g)peaklinearacceleration,PRA(rad/s2)peakrotational
accelerationsin
radians/
second/second(rad/s2),Profprofessional,Reconreconstructed,RU
rugbyunion,SD
standarddeviation,SnrAmatsenioram
ateur,Ttotalim
pacts,#male,
$female
aCalculatednumber
ofim
pacts
bMedianresults
cContact
practice
dMale
eFem
ale
Reporting Impact Data in Sport 159
123
transferred to an Excel spreadsheet for further analysis. All
matches were videotaped (Sony HDR-PJ540 Camcorder)
to enable verification of the impacts recorded.
The biomechanical measures of head impact severity
consisted of impact duration in milliseconds (ms), linear
acceleration (g), and rotational head acceleration (rad/s2).
Resultant linear acceleration is the rate of change in
velocity of the estimated centre of gravity of the head
attributable to an impact and the associated direction of
motion of the head [107]. Resultant rotational acceleration
is the rate of change in rotational velocity of the head
attributable to an impact and its direction in a coordinate
system with the origin at the estimated centre of gravity of
the head [107]. The rotational acceleration was calculated
through the IMS utilising a 5-point stencil from the rota-
tional velocity measured by the tri-axial angular rate
gyroscope (L3G4200D; ST Microelectronics, Geneva,
Switzerland; http://www.st.com).
Impacts were identified as any linear acceleration above
10 g measured at the mouthguard. These impacts could be
a result of a direct blow to the head, face, neck or else-
where on the body with an ‘impulsive’ acceleration trans-
mitted to the head. Each recorded impact was categorised
into four general locations (front, side, back and top) [53].
The direction of impact (azimuth h) was defined from
-180� to 180� with 0� at the x axis with positive h on the
right side of the player’s head. The height of impact (ele-
vation a) was defined from 0� (horizontal plane that passesthrough the head’s centre of gravity) to 90� (crown of the
head at the z axis). The xz plane represented the midsagittal
plane, with positive x corresponding to the caudal direc-
tion. The xy plane represented the coronal plane, with
positive y corresponding to the right side of the head.
Impacts with an a of [65� were defined as top, while
impacts with a h of -45� to 45� were defined as back,
±45� to ±135� side and -135� to 135� front.All impacts recorded were assessed for head movement
or biting by the players when the mouthguard was worn.
Impacts that were identified as having occurred through
these activities were termed ‘clacks’. All impacts were
assessed through the IMS utilising a ‘de-clacking algo-
rithm’ that involved two methods. The first method utilised
various parameters (time above 10-g data acquisition limit,
ratio of PLA to area under curve, filtered/unfiltered PLA
ratio, PLA vs. number of points above data acquisition
limit) to assess waveform characteristics of the aggregate
of the various features of the acceleration waveform to
determine an impact versus a ‘clack’. The second method
utilised a cross-correlation pattern matching (with config-
urable cross-correlation coefficient) by comparing the
impact form to a Gaussian-like reference waveform,
looking for a cross-correlation coefficient above a config-
urable data acquisition limit (0.90 is the default, i.e. 90 %
match). This method assumed a ‘good’ shape for a head
impact and matches recorded impacts against this reference
waveform (personal correspondence, J. Thibado; 1 May
2014). All impacts identified as ‘clacks’ were removed
from the data set prior to downloading for further analysis.
All data collected were entered into a Microsoft Excel
spreadsheet and analysed with SPSS V.22.0.0.
Over the course of the 2013 domestic rugby union
season of matches, a total of 20,687 impacts exceeded our
study data acquisition limit of 10 g for a head impact and
were retained for data analyses. Impacts of\10 g of linear
acceleration were considered negligible in regard to impact
biomechanical features and to eliminate head accelerations
from non-impact events such as jumping and running [55].
Their relationships to head trauma make it difficult to
distinguish between head impacts and voluntary head
movement [107]. Please see King et al. [27] for the full
statistical analysis undertaken on the head impact
biomechanics.
2.3 Application of Head Impact Thresholds
Identified from the Literature to the Rugby
Head Impact Data Set
The data set used for the application of the head impact
thresholds identified from the literature review was from 38
amateur rugby union players who wore instrumented
mouthguards over a season of matches [27]. The raw data
set was filtered by linear acceleration thresholds at incre-
ments of 1 g to establish the percentage of impacts
removed at each threshold from 10.0 to 30.0 g. This per-
centage was then used to calculate the possible number of
impacts removed for the impact thresholds used in the
different studies reviewed.
All data estimations were calculated on an Excel
spreadsheet. The data were analysed using SPSS v22.0.0
(SPSS Inc.), and as the data were non-normally distributed
(Shapiro–Wilk test p\ 0.001), data were analysed using a
Friedman repeated measures ANOVA on ranks. Post hoc
analysis with Wilcoxon signed-rank tests was conducted
with a Bonferroni correction applied. Statistical signifi-
cance was set at p\ 0.05. The estimated number of
impacts was calculated by dividing the number of reported
impacts by the estimated percentage of impacts removed at
the different thresholds. The estimated total number of
reported impacts was subtracted from the reported number
of impacts to identify the possible number of impacts
removed from the data set; for example, for a number of
impacts reported of 161,732 [87, 88], an impact threshold
of 14.4 g, and based on the New Zealand rugby union data
set for 20,687 impacts recorded at 10.0 g, when reassessed
at 14.4 g, there were 12,091 impacts. A total of 8569
impacts were removed or 42 % of the data set (see Fig. 2).
160 D. King et al.
123
Therefore, 161,732 (number of impacts reported) 7 42 %
(percentage of impacts removed at 14.4 g) gave a possible
total number of impacts at the 10-g threshold of 385,076.
The possible total number of impacts removed from the
data set was 223,344 (i.e. 385,076 - 161,732 impacts).
3 Findings
3.1 Literature Review
A total of 52 publications were identified that reported head
impacts and met the inclusion criteria. Studies reported
impacts to the head via technology in American football
[12, 16, 20–23, 32, 37, 42, 49, 51–54, 57, 58, 61, 71, 73,
76–79, 81, 85–95, 97, 98, 100–102], ice hockey [24, 25, 80,
82–84, 96, 101, 103], soccer [26], rugby union [10, 27],
Australian Football [10, 99] and mixed martial arts and
boxing [73].
3.1.1 Impact Threshold
Studies utilised different data impact acceleration
thresholds (see Table 1): 42 % of studies [21, 22, 24, 26,
27, 49, 52, 54, 61, 73, 76–86] used 10 g; 17 % of studies
[20, 23, 42, 51, 53, 58, 87–90] used 14.4 g; 8 % of studies
[37, 91–95] used 15 g; 4 % of studies [25, 96] used 20 g;
2 % of studies [97] used 30 g; and 4 % of studies [32, 98]
reported impact data within 10–60 g and greater than
90 g. Four studies [10, 12, 16, 99] (8 %) were
reconstruction studies from video analysis, but were
included as they reported impact biomechanics. Six
studies [57, 71, 100–103] (12 %) did not report the impact
threshold, but did report head impact biomechanics. One
study [73] (2 %) used a 7- and 10-g threshold with dif-
ferent sporting activities.
3.2 Acceleration Raw Data and Metrics
Apart from raw resultant linear accelerations [12, 16, 20–
27, 32, 37, 49, 52, 54, 57, 58, 61, 71, 73, 76–103] (reported
in 90 % of studies) and rotational acceleration data [10, 51]
(reported in 73 % of studies) [10, 12, 16, 20–27, 37, 51, 54,
57, 58, 71, 73, 77, 78, 81–96, 99–101, 103], several head
impact-derived variables were reported, such as the GSI
[64] (4 % [26, 49]), the HIC [68] (21 % [10, 12, 16, 24, 26,
49, 71, 79, 87, 88]), HITSP [71] (28.8 % [21, 25, 37, 71, 78,
81, 83–86, 89, 93–96, 103]) and the RWE [58] (1.8 %)
metrics.
Nearly all of the studies reviewed identified the number
of impacts that were recorded; however, 4 % of studies
reported impacts in matches only, 23 % recorded impacts
for both match and practice activities, and 55 % combined
both match and practice activity impacts. The remaining
15 % of studies reviewed reported on impacts above
90 g or were reconstruction of impacts from video analysis.
The number of impacts ranged from 480 impacts from 22
players in Pop Warner American football [97] to 486,594
impacts from 450 players in collegiate American football
and ice hockey [101] (see Table 1).
-90
-80
-70
-60
-50
-40
-30
-20
-10
010g 11g 12g 13g 14g 15g 16g 17g 18g 19g 20g 21g 22g 23g 24g 25g 26g 27g 28g 29g 30g
Perce
ntage
of im
pacts
lost
from
10g
Data collection impact threshold
45% impacts removed at 15.0g
42% impacts removed at 14.4g
81% impacts removed at 30.0g
Fig. 2 Percentage of impacts
removed when applying
different data impact threshold
limits compared with original
10-g threshold limit for the New
Zealand data set of head impacts
to senior amateur rugby union
players for one season
Reporting Impact Data in Sport 161
123
Over half (52 %) of the studies [10, 12, 16, 22, 23, 27,
37, 49, 58, 61, 76, 77, 80–86, 91, 93–98, 102, 103] reported
the impact biomechanics data as mean ± SD. Some studies
[20, 21, 23, 25, 54, 58, 73, 85, 90, 94, 100] (21 %) also
reported the head impacts as median, but not all [20, 21, 23,
54, 85, 90, 100] (13 %) included the interquartile ranges
for the data. Of the studies that reported the impact
biomechanics by the median, only 8 % [25, 58, 73, 94]
reported the interquartile range. Most of the studies
reporting the median also reported the 95th percentile of
the impacts. Other data reporting methodologies utilised
within the data sets reviewed were the median of the 95th
[21], 98th [71, 94], 99th [71, 94], and 99.5th [94] per-
centiles. Fourteen percent of studies also included lower
and upper limits [61, 83, 84, 86] for the range of impacts
[24, 101] and the mean range [97] of the impacts. Less than
a quarter of studies (23 %) reported their impacts as x-, y-,
z-axis data [22], ?1 SD [52], cumulative distribution
functions [54, 58], percentage of impacts [21, 53], and the
impact duration (ms) [16, 87, 88, 92, 93]. In addition to the
impact biomechanics being presented by various method-
ologies, 14 % of studies [12, 27, 37, 81, 86, 91, 102] also
incorporated impact tolerances and impact severity levels.
3.3 Application of Head Impact Thresholds
to the Rugby Head Impact Data Set
By utilising data from a previously published study [27]
that used the 10-g impact threshold, data were re-extracted
at differing impact thresholds from 10 g to 30 g. By
adjusting the impact threshold (see Fig. 2), the number of
impacts decreased as the impact threshold increased (see
Table 2). There were significant differences observed
(p\ 0.05) for each of the different acceleration thresholds
for the number of impacts reported, the mean, median and
the 95th percentile when compared with the impacts at the
10-g linear acceleration threshold (see Table 2).
Based on the differences observed in the study reporting
on impacts in amateur senior rugby union [27], at the 14.4-
g threshold, there could have been as many as 42 % of the
impacts recorded not being reported. As a result, studies
[20, 23, 25, 32, 37, 42, 51, 53, 58, 87–98] using impact
thresholds above 10 g may have removed 2100–206,573
impacts. At the 30-g impact threshold, it can be estimated
that 80–85 % of impacts were not reported [97]. Again,
based on the differences observed in this study through the
analysis of different thresholds, it is possible that each
player in the Pop Warner study [97] may have experienced
a cumulative total of 1885 impacts above 10 g. Although
the impacts may not have been recorded, the players may
well have been exposed to this number of impacts between
10 and 30 g. The differences between impacts reported and
the possible number of impacts (480 vs. 2365) may result
in an underestimation of the exposure risk of these players
to subconcussive impacts.
4 Discussion
This study undertook to review the methods for reporting
head impact data in sport and to outline the effect of var-
ious acceleration thresholds on the number of impacts
reported. A consensus on a threshold for reporting data is
important given the variation in conclusions that may be
drawn if the same data set is used with different thresholds,
as identified by our application of the range of thresholds
from prior literature applied to a New Zealand rugby union
head impact data set. A standard threshold for head impact
data is important given possible monitoring of player head
impact acceleration data in the hope of identifying a
cumulative threshold for concussion from subconcussive
impacts.
The equipment utilised to record and report head
impacts varies in sensitivity and the types of algorithms
employed for the identification of impacts [62]. These
differences may invariably influence the results of the
published studies as, although some studies report the lin-
ear threshold as 14.4 g, they may actually be recording
from 10 g and, if the researcher is unaware that this
threshold is the default, the data may be included (personal
correspondence, S. Broglio; September 2015). In the
recording of data for the HITS, the data are based on the
triggering of one accelerometer, and the unfiltered/unpro-
cessed data only loosely relates to the final measurement of
interest at the head’s centre of gravity.
The discussion surrounding subconcussive impacts has
become popular [32, 41, 43, 95, 108, 109]. Initially the
term subconcussive impact described an impact that did not
result in severe, noticeable symptoms, especially loss of
consciousness [108]. However, recently, subconcussive is a
term used to describe an asymptomatic non-concussive
impact to the head [32, 41, 43, 95, 109]. The issue relating
to the effects of subconcussive impacts is controversial as
researchers and clinicians are divided on the true effects
[30–32, 42, 45, 110]. Some research [32, 110] has reported
that these impacts have minimal effect on cognitive func-
tions, while others [30, 31, 42, 45, 46] have reported these
impacts to be detrimental to cerebral and cognitive func-
tions. To date, there is a paucity of evidence to identify the
impact acceleration that is adequate to produce a non-
structural brain injury associated with the neuronal changes
of concussion [30].
Animal models display metabolic changes associated
with concussion, which may be similar in subconcussive
impacts [111]. To research subconcussive impacts in iso-
lation is challenging, and there are, to date, no reports on
162 D. King et al.
123
Table
2Differencesin
theresultantPLA(g),PRA(rad/s2),HIC
15andGSIatdifferentim
pactthreshold
limitsbythemean(±
SD),median[25th
to75th
percentile]and95th
percentileforthe
New
Zealandsenioram
ateurrugbyuniontotalplayer
dataset
Dataacquisition
impactthreshold
(g)
Noof
impacts
ResultantPLA
(g)
ResultantPRA
(rad/s2)
HIC
15
GSI
Mean±
SD
Median
[25th–
75th]
95%
Mean±
SD
Median[25th–75th]
95%
Mean±
SD
Median
[25th–75th]
95%
Mean±
SD
Median
[25th–75th]
95%
10
20,687
22±
16
16[12–26]
53
3903±
3949
2625[1324–4934]
12,204
32±
99
9[5–25]
128
48±
118
15[8–398]
192
11
17,747
24±
17
18[13–29]
56
4255±
4096
2898[1549–5389]
12,945
37±
106
11[6–30]
145
55±
126
19[10–47]
218
12
15,454
26±
17
20[15–31]
59
4603±
4214
3181[1781–5860]
13,581
42±
112
14[7–35]
160
62±
134
23[12–55]
241
13
13,825
28±
17
22[16–32]
62
4858±
4293
3423[1967–6263]
13,948
46±
118
17[9–40]
176
69±
140
27[14–62]
262
14
12,531
29±
18
24[18–34]
64
5079±
4368
3589[2123–6596]
14,325
51±
123
19[10–44]
188
75±
146
31[17–69]
278
15
11,459
31±
18
25[19–35]
65
5286±
4438
3774[2263–6908]
14,647
55±
128
22[12–49]
205
80±
151
34[19–76]
297
16
10,570
32±
18
26[20–36]
67
5478±
4510
3936[2400–7180]
14,994
59±
133
24[14–53]
215
86±
156
38[21–82]
318
17
9784
33±
18
27[21–38]
68
5655±
4565
4082[2538–7394]
15,235
63±
137
27[15–57]
228
92±
161
41[24–88]
331
18
9095
34±
18
28[22–39]
70
5799±
4610
4173[2644–7567]
15,486
67±
141
29[17–62]
241
97±
165
45[27–95]
348
19
8500
35±
19
29[23–40]
71
5939±
4662
4265[2731–7744]
15,823
70±
145
32[18–66]
253
103±
169
49[29–102]
364
20
7934
36±
19
30[24–41]
74
6072±
4716
4357[2810–7931]
16,256
74±
150
34[20–70]
263
109±
174
53[31–109]
374
21
7430
37±
19
31[25–42]
76
6206±
4757
4483[2896–8158]
16,470
79±
154
37[22–75]
275
115±
178
57[34–114]
391
22
6938
39±
19
32[26–44]
77
6363±
4801
4595[2992–8426]
16,806
83±
158
40[24–80]
291
121±
183
62[37–121]
415
23
6463
40±
19
33[27–45]
80
6519±
4859
4722[3096–8628]
17,073
88±
163
43[26–85]
302
127±
188
67[40–129]
444
24
6060
41±
19
34[28–46]
82
6656±
4906
4835[3201–8798]
17,282
92±
167
46[28–90]
318
134±
192
71[43–135]
466
25
5666
42±
20
35[29–47]
83
6819±
4952
4965[3305–9012]
17,435
97±
172
49[31–95]
337
141±
197
76[47–144]
485
26
5275
43±
20
36[30–48]
84
6977±
4986
5101[3428–9297]
17,622
102±
177
53[33–101]
357
148±
202
81[50–152]
512
27
4955
44±
20
37[31–49]
87
7107±
5036
5210[3495–9459]
17,844
107±
181
57[35–107]
389
155±
206
86[54–162]
536
28
4642
45±
20
39[32–51]
88
7261±
5079
5339[3607–9704]
18,131
113±
186
60[38–114]
396
163±
211
93[58–173]
557
29
4305
47±
20
40[33–52]
91
7448±
5130
5492[3778–9917]
18,221
119±
192
65[41–123]
407
172±
217
99[64–186]
583
30
4024
48±
20
41[34–54]
92
7597±
5187
5624[3875–10,129]
18,436
125±
197
69[44–131]
420
180±
221
106[68–196]
606
Therewas
asignificantdifference
(p\
0.05)forallvalues[
10gwhen
compared
withthevalues
atthe10gthreshold
GSIGaddSeverityIndex,HIC
15HeadIm
pactCriterion15ms,PLA(g)peaklinearacceleration(w
heregistheaccelerationofgravity),PRA(rad/s2)peakrotational
accelerationin
radians/
second/second(rad/s2),SD
standarddeviation
Reporting Impact Data in Sport 163
123
animal models or other reliable methodologies that have
been successful at identifying these impacts [111]. Brain
injury may occur from concussive events as well as from
an accumulation of subconcussive impacts [41]. The
effects of concussive events and multiple subconcussive
impacts have been associated with long-term progressive
neuropathologies and cognitive deficits [43, 112–114].
Longitudinal impact monitoring at the level where these
subconcussive events are beginning to occur is important,
and a standard threshold needs to be established.
4.1 What Threshold Should be Used to Monitor
Head Impacts?
Impacts of \10 g of linear acceleration have been con-
sidered negligible in regards to impact biomechanical
features. The \10-g impact threshold has been used in
research to eliminate head accelerations from non-impact
events such as jumping and running [21, 55, 61]. The
inclusion of these non-impact events to head trauma make
it difficult to distinguish between head impacts and vol-
untary head movement [107], and eliminating these will
help identify the true extent of the number of impacts that
do occur from sports participation. A suggestion for this
may be to report the distribution of the impacts by the
various resultant linear accelerations, using a frequency
analysis and reporting quartile ranges, i.e. 25th and 75th
interquartile range. This may assist in identifying where the
most frequent resultant linear accelerations occur in the
different sports. Consensus for the impact threshold will
need to be established, and should be based on validation
studies to determine the best impact threshold for various
sports and injury outcomes. Biomechanical modelling of
impact forces and brain movement would be needed to
identify likely impact thresholds for injury, as well as in-
field validation studies using prospective monitoring of
players during tackles and impacts with the ground. As
there is no established criterion for reporting head impact
biomechanics, and the largest proportion (42 %) of studies
[21, 22, 24, 26, 27, 49, 52, 54, 61, 72, 75–85] reported the
resultant linear acceleration threshold at 10 g, future
studies should report all impacts above the 10-g resultant
linear acceleration threshold.
4.2 What Descriptive Statistics Should be Used
to Report Head Impact Biomechanics?
There were a variety of descriptive statistics used in the
reporting of head impact biomechanics in the reviewed
studies which limits inter-study comparisons. Although
more than half (52 %) of the studies reviewed [10, 12, 16,
22, 23, 27, 37, 49, 58, 61, 73, 76, 77, 81–87, 91, 94–98,
102, 103] reported their results by means and SDs, the use
of these statistics may not accurately represent the true
centre of the data. By reporting the mean value of the data
set, this method is subject to extreme values (i.e. outliers)
such as those in skewed data sets. The use of the mean is
suitable if the data set has a symmetrical distribution. In
non-normal distributed data, the median is the most useful
for describing the centre of the data [127]. Of the studies
[23, 25, 58, 85, 87, 94] reviewed (22 %) that reported the
results by the median would more accurately have identi-
fied the centre of the data set. The New Zealand senior
amateur head impact data were non-normally distributed
(i.e. not symmetrical), therefore the use of descriptive
statistics that can account for this skewness needed to be
considered. To enable inter-study comparisons, and until a
consensus is established for the reporting of head impact
biomechanics, future studies should report the median
[25th and 75th interquartile ranges] for all head impact
biometrics.
4.3 What Acceleration Metrics Should be Used
to Monitor Head Impacts?
It has been suggested that both resultant linear and rota-
tional accelerations should be reported with head impact
metrics [115], as there is an improved correlation between
impact biomechanics and the occurrence of a concussion,
compared with when linear accelerations are reported alone
[12]. Research [18, 116–119] suggests that the brain is
more sensitive to rotational than linear accelerations.
Rotational accelerations are reported [12, 120] to be cor-
related to the strain response of the brain and the primary
mechanism for diffuse brain injury including concussion,
contusion, axonal injuries and loss of consciousness [116,
117, 121, 122]. Linear accelerations are reported to result
in the intracranial pressure response of the brain and to be
the primary mechanism for skull fractures and epidural
haematomas [120, 123]. Reporting both linear and rota-
tional accelerations should assist with identification of
possible brain injury.
More recently [57, 58], resultant linear and rotational
acceleration results have been combined into an RWE
metric. This metric can be beneficial for fully capturing the
linear (RWELinear), rotational (RWERotational) and com-
bined probability (from linear and rotational) (RWECP) of
the risk of a concussion as it accounts for the frequency and
severity of each player’s impacts. The HIC and GSI are the
most frequently utilised head injury assessment functions
in helmet and traffic restraint safety standards [12, 124];
however, this was not reflected in the sport head impact
studies reviewed. Based on the WSTC [68], the HIC and
GSI criteria are considered plausible ways of determining
relative risk of severe head injury [125], but they do not
account for the complex motion of the brain, nor the
164 D. King et al.
123
contribution of resultant rotational acceleration to the head
[12, 14, 74]. In particular, the HIC only deals with frontal
impacts and was not designed to be used for lateral impacts
that can be found in head impact biomechanics [124] and
arbitrarily defines an ‘unsafe pulse’ within a ‘safe pulse’ by
discounting any data outside the two time points chosen for
the calculation of the HIC value [126].
The GSI and HIC may be beneficial for evaluating acute
head trauma due to single impacts, but they are reportedly
not beneficial for repeated impacts at lower acceleration
magnitudes [124], such as those found in contact sports
such as American football, rugby union and soccer. The
inclusion of the HIC and GSI by studies reporting on head
impact biomechanics may be more historical, thus pro-
viding the ability for inter-study comparisons with previous
studies. However, as they are used to calculate multiple
impacts and provide a nonsensical number, the value of
these metrics is limited. The use of HIC and GSI in future
studies, and the value that these metrics provide, needs to
be standardised. Consensus is required on the incorporation
of these and other biomechanical metrics into future
research.
4.4 Limitations in the Use of Accelerometery
The use of accelerometers to record and assess movement
is not new to the scientific community [127, 128]. There
have been some inter-study and international comparability
limitations reported for use of accelerometers to report
physical activity [63]. The identified limitations for phys-
ical activity accelerometers may be identical to areas now
being faced by studies reporting the biomechanics of
impacts to the head. The largest proportion of studies
reporting head impact biomechanics have utilised HITS
[20–24, 32, 37, 42, 49, 51–54, 57, 58, 61, 71, 76–79, 81–
95, 97, 98, 100–102], or a variant [26]. More recently, an
electronic mouthguard has been used to assess head
impacts in rugby union [27].
The issues identified with the use of accelerometers for
physical activity [63] include affordability of the
accelerometers [63], and the administration burden [63] to
the participants and researcher(s) given post-data-collec-
tion analysis. The choice of accelerator brand [129], gen-
eration [130] and firmware version [131], wearing position
[132] based on the sports code requirements (i.e. helmet
mounted vs. headband mounted vs. mouthguard embedded
vs. patch), specifics of the research being undertaken, such
as the epoch length [133, 134] (match vs. training vs.
combined), data imputation methods [135], dealing with
spurious data [136] and the reintegration of smaller epochs
into larger epochs [137] are all considerations for use of
accelerometers. In addition to the issues identified, there
are technological developments, emerging methodological
questions and a lack of academic consensus that may
also hinder the development of uniformity in the utilisation
of accelerometers [63] for recording head impact
biomechanics.
In comparing the New Zealand rugby union data with
data collected with the use of the HITS, it must be noted
that these are different impact telemetry systems. The
mouthguard is reported to have a 10 % error for linear and
rotation acceleration and for angular velocity with an
average offset of 2� for azimuth and elevation impact
location [104, 105]. Although the correlation of the AIM
mouthguard with laboratory head forms is good, the impact
measurements should be assumed to have some form of
error that is dependent on impact conditions and the mea-
sure of interest and the variability tested [101, 138]. It is
unlikely that the mouthguard was tested under all of the
activities seen in rugby union matches, such as the rucks,
mauls, lineouts and scrum situations. How these rugby
activities correlate to the laboratory conditions is unknown.
Although the majority of the impact biomechanics studies
reported in this review are helmet-based telemetry systems,
there is a paucity of studies reporting on head impact
biomechanics with other systems such as the mouthguard
and headband. In addition, there are no published studies
comparing the HITS with other forms of impact telemetry
systems, such as the X2Biosystems AIM mouthguard.
A final consideration to the use of accelerometers in
recording impacts is the need for concurrent video analysis
to enable comparison and verification of the impacts. This
would enable the identification of non-impact activities
where an impact has been recorded, such as post-try cel-
ebrations, dropping equipment onto the ground, or other
activities where the equipment may record an impact. In
the case of the New Zealand rugby union data, only
impacts that occurred in the tackle with the player standing
were able to be verified [27]. The percentage of impacts
that were identified at the 10-g inclusion limit, that were
able to be visualised by video review and analysis, varied
from 65 to 85 % of the total impacts recorded per match
[27].
4.5 What are the Long-Term Implications
of Repeated Head Impacts?
The use of impact tolerance and impact severity level data
may be important if a risk assessment is undertaken for
possible long-term implications from repetitive head
impacts (RHIs). Recently, in a small sample [77] of col-
legiate players with no reported concussions after a season
of American football, there were white-matter changes that
correlated with multiple head impact measures. Partici-
pants with more than 30–40 RHIs with PRAs of
[4500 rad/s2 per season (r = 0.91; p\ 0.001) and more
Reporting Impact Data in Sport 165
123
than 10–15 RHIs with[6000 rad/s2 (r = 0.81; p\ 0.001)
were significantly correlated with post-season white-matter
changes [77]. These changes post season imply a rela-
tionship between the number of RHIs that occur over a
season of American football and white-matter injury,
despite no clinically evident concussion being recorded
[77].
The inclusion of impact tolerances and impact severity
levels may assist with the identification of players at risk of
possible long-term injuries. Impact tolerance may also act
as an indicator of when to rest players if they are exposed
to RHIs above [4500 and [6000 rad/s2. This type of
information will assist in formulating a detailed under-
standing of the exposure and mechanism of injury of
concussion [53, 139]. Further research is required to
evaluate the injury tolerance of concussive type injuries, to
develop interventions to reduce the likelihood of any
concussive type injuries, and to develop exposure durations
and standdown periods to establish a broader understanding
of the potential role of subconcussive events and long-term
health [53].
5 Conclusion
This study identified the methodological differences in the
threshold limits of impacts to the head as a result of par-
ticipation in contact sports. Of the 52 studies, 42 % reported
impacts at the 10-g impact threshold, while 17 % of studies
used the 14.4-g impact threshold. Resultant linear acceler-
ations were most frequently reported (90 %), while 73 %
reported resultant rotational accelerations. Over three-
quarters (94 %) of studies reported both resultant linear and
rotational accelerations. Impact data were most frequently
(52 %) reported as mean ± SD. Some studies (21 %)
reported the head impact data as median, but not all (13 %)
included the interquartile ranges for these data.
The influence of head impact thresholds was shown
using head impact data obtained from 38 senior amateur
rugby union players during 19 matches in New Zealand.
Application of the varied impact thresholds resulted in
20,687 impacts of[10 g, 11,459 (44.6 % less) impacts of
[15 g, and 4024 (80.5 % less) impacts of[30 g.
Given head impacts are likely to cause concussive
injury, and the number of head impacts may be related to a
potential concussion threshold (i.e. a cumulative thresh-
old), the number and severity of head impacts should be
monitored in players. However, impacts can be measured
with several technologies (e.g. instrumented behind-the-ear
patches, mouthguards and head gear), and thresholds have
differed for reporting impact data in contact and collision
sports. Consensus is therefore required to identify the
reporting modalities (e.g. linear threshold, descriptive
calculations) to utilise in future impact studies to enable
between-study comparisons. Until in-field validation stud-
ies are completed, it is recommended that data should be
reported as mean ± SD and median and interquartile ran-
ges, using the 10-g impact threshold.
Acknowledgments The authors declare that there are no competing
interests associated with the research contained within this manu-
script. No sources of funding were utilised in conducting this study.
According to the definition given by the International Committee of
Medical Journal Editors (ICMJE), the authors listed above qualify for
authorship on the basis of making one or more of the substantial
contributions to the intellectual content of the manuscript.
Compliance with Ethical Standards
Conflicts of interest Doug King, Patria Hume, Conor Gissane, Matt
Brughelli and Trevor Clark declare that they have no conflict of
interest.
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